Journal ArticleDOI
Cellular neural networks: theory
Leon O. Chua,L. Yang +1 more
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TLDR
In this article, a class of information processing systems called cellular neural networks (CNNs) are proposed, which consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly through their nearest neighbors.Abstract:
A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear analog circuits that process signals in real time. Like cellular automata, they consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly only through their nearest neighbors. Each cell is made of a linear capacitor, a nonlinear voltage-controlled current source, and a few resistive linear circuit elements. Cellular neural networks share the best features of both worlds: their continuous-time feature allows real-time signal processing, and their local interconnection feature makes them particularly adapted for VLSI implementation. Cellular neural networks are uniquely suited for high-speed parallel signal processing. >read more
Citations
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A 125 GOPS 583 mW Network-on-Chip Based Parallel Processor With Bio-Inspired Visual Attention Engine
TL;DR: A network-on-chip (NoC) based parallel processor is presented for bio-inspired real-time object recognition with visual attention algorithm, which achieves a peak performance of 125 GOPS and 22 frames/sec object recognition while dissipating 583 mW at 1.2 V.
Journal ArticleDOI
Toward visual microprocessors
TL;DR: In this paper, the authors outline motivations and models underlying the design of visual microprocessors based on the cellular neural network universal machine and overview the state of the art regarding the realization of these micro-processors in the form of very large-scale integration chips.
Journal ArticleDOI
Global exponential stability analysis of Cohen-Grossberg neural networks
TL;DR: A general sufficient condition ensuring global stability of the neural networks is derived by constructing a novel Lyapunov functional and carefully estimating its derivative, which improves some existing conditions and generalize and unify some previous results.
Journal ArticleDOI
STT-SNN: A Spin-Transfer-Torque Based Soft-Limiting Non-Linear Neuron for Low-Power Artificial Neural Networks
TL;DR: This paper proposes a spin-transfer-torque (STT) device based on domain wall motion (DWM) magnetic strip that can efficiently implement a soft-limiting non-linear neuron (SNN) operating at ultra-low supply voltage and current and presents an ANN hardware design employing the proposed STT-SNNs and memristor crossbar arrays (MCA) as synapses.
Journal ArticleDOI
Image encryption method based on chaotic fuzzy cellular neural networks
TL;DR: Evaluations on standard test images verified and confirmed that the proposed encryption method is robust against plaintext-only (i.e., brutal force) and chosen-plaintext attacks.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Journal ArticleDOI
Neurons with graded response have collective computational properties like those of two-state neurons.
TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Book
Neurons with graded response have collective computational properties like those of two-state neurons
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.